Control device, control program, and control method

Abstract

According to one embodiment, a control device that controls operation of a system includes a first selecting module, a second selecting module, a control error measuring module, a determining module, and a control module. The first selecting module selects a first neural network from neural networks different in network configuration from each other. The second selecting module selects a second neural network different from the first neural network from the neural networks. The control error measuring module measures first control error in control by the first neural network and second control error in control by the second neural network. The determining module compares the first control error and the second control error measured by the control error measuring module, and determines a neural network with less control error. The control module controls the operation of the system by the neural network with less control error determined by the determining module.

1 . A control device that controls operation of a system, comprising:
 a first selecting module configured to select a first neural network from a plurality of neural networks which are different in network configuration from each other;   a second selecting module configured to select a second neural network different from the first neural network from the neural networks;   a control error measuring module configured to measure first control error in control by the first neural network and second control error in control by the second neural network;   a determining module configured to compare the first control error and the second control error measured by the control error measuring module, and determine a neural network with less control error; and   a control module configured to control the operation of the system by the neural network with less control error determined by the determining module.   
     
     
         2 . The control device according to  claim 1 , wherein the neural networks are each configured of a plurality of nodes and links between the nodes, the control device further comprising:
 a weight correcting module configured to correct weight set to the links in the neural network used by the control module to control the operation of the system based on the first control error or the second control error measured by the control error measuring module;   a link excluding module configured to exclude links where a value based on the weight is smaller than or equal to a first threshold value from the links weight of which is corrected by the weight correcting module; and   a link introducing module configured to introduce links where a value based on the weight is larger than a second threshold value different from the first threshold value from the links excluded by the link excluding module.   
     
     
         3 . The control device according to  claim 1 , further comprising a detecting module configured to detect disturbance of the system, wherein
 the second selecting module is configured to select the second neural network based on a parameter of the disturbance detected by the detecting module.   
     
     
         4 . The control device according to  claim 1 , further comprising a plurality of shock sensors configured to detect disturbances of the system, wherein
 the first neural network is associated with a combination of parameters of the disturbances detected by the shock sensors, and   the second control error of the second neural network is lowest among control errors of the neural networks other than the first neural network.   
     
     
         5 . The control device according to  claim 4 , further comprising an associating module configured to associate the combination of the parameters of the disturbances associated with the first neural network and the second neural network when the determining module determines that the second control error is lower than the first control error. 
     
     
         6 . The control device according to  claim 1 , wherein the control module is configured to refer to a look-up table, in which an input value in a function related to the neural network and a function value based on the input value are associated with each other, in calculation related to the neural network, and obtains the function value associated with the input value using the input value as an argument. 
     
     
         7 . The control device according to  claim 1 , wherein
 the system is a disk device, and   the control module is configured to control a voice coil motor of the disk device.   
     
     
         8 . A computer program product embodied on a computer-readable medium and comprising code to control operation of a system, the code, when executed, causing a computer to perform:
 first selecting a first neural network from a plurality of neural networks which are different in network configuration from each other;   second selecting a second neural network different from the first neural network from the neural networks;   measuring first control error in control by the first neural network and second control error in control by the second neural network;   comparing the first control error and the second control error measured at the measuring to determine a neural network with less control error; and   controlling the operation of the system by the neural network with less control error.   
     
     
         9 . The computer program product according to  claim 8 , wherein
 the neural networks are each configured of a plurality of nodes and links between the nodes, and   the code further causing the computer to perform:
 correcting weight set to the links in the neural network used to control the operation of the system at the controlling based on the first control error or the second control error measured at the measuring; 
 excluding links where a value based on the weight is smaller than or equal to a first threshold value from the links weight of which is corrected at the correcting; and 
 introducing links where a value based on the weight is larger than a second threshold value different from the first threshold value from the links excluded at the excluding. 
   
     
     
         10 . The computer program product according to  claim 8 , wherein
 the code further causing the computer to perform detecting disturbance of the system, and   the second selecting includes selecting the second neural network based on the disturbance of the system detected at the detecting.   
     
     
         11 . The computer program product according to  claim 8 , wherein
 the code further causing the computer to perform detecting disturbances of the system by a plurality of shock sensors,   the first neural network is associated with a combination of parameters of the disturbances detected at the detecting, and   the second control error of the second neural network is lowest among control errors of the neural networks other than the first neural network.   
     
     
         12 . The computer program product according to  claim 11 , wherein the code further causing the computer to perform associating the combination of the parameters of the disturbances associated with the first neural network with the second neural network when it is determined that the second control error is lower than the first control error. 
     
     
         13 . The computer program product according to  claim 8 , wherein the controlling includes referring to a look-up table, in which an input value in a function related to the neural network and a function value based on the input value are associated with each other, in calculation related to the neural network, and obtaining the function value associated with the input value using the input value as an argument. 
     
     
         14 . The computer program product according to  claim 8 , wherein
 the system is a disk device, and   the controlling includes controlling a voice coil motor of the disk device.   
     
     
         15 . A control method of controlling operation of a system, comprising:
 first selecting a first neural network from a plurality of neural networks which are different in network configuration from each other;   second selecting a second neural network different from the first neural network from the neural networks;   measuring first control error in control by the first neural network and second control error in control by the second neural network;   comparing the first control error and the second control error measured at the measuring to determine a neural network with less control error; and   controlling the operation of the system by the neural network with less control error.   
     
     
         16 . The control method according to  claim 15 , wherein the neural networks are each configured of a plurality of nodes and links between the nodes, the method further comprising:
 correcting weight set to the links in the neural network used to control the operation of the system at the controlling based on the first control error or the second control error measured at the measuring;   excluding links where a value based on the weight is smaller than or equal to a first threshold value from the links weight of which is corrected at the correcting; and   introducing links where a value based on the weight is larger than a second threshold value different from the first threshold value from the links excluded at the excluding.   
     
     
         17 . The control method according to  claim 15 , further comprising detecting disturbance of the system, wherein
 the second selecting includes selecting the second neural network based on the disturbance of the system detected at the detecting.   
     
     
         18 . The control method according to  claim 15 , further comprising detecting disturbances of the system by a plurality of shock sensors, wherein
 the first neural network is associated with a combination of parameters of the disturbances detected at the detecting, and   the second control error of the second neural network is lowest among control errors of the neural networks other than the first neural network.   
     
     
         19 . The control method according to  claim 18 , further comprising associating the combination of the parameters of the disturbances associated with the first neural network with the second neural network when it is determined that the second control error is lower than the first control error. 
     
     
         20 . The control method according to  claim 15 , wherein the controlling includes referring to a look-up table, in which an input value in a function related to the neural network and a function value based on the input value are associated with each other, in calculation related to the neural network, and obtaining the function value associated with the input value using the input value as an argument.